CHAIR OF

MANAGEMENT

SCIENCE

The goal of this project is to systematically improve quantitative decision support for urban mobility and logistics, to analyze its methodological functionality, to derive general conceptual insight, and to use the derived concept for future method designs.For applications in urban mobility and logistics, operational decision support needs to be effective, fast, and applicable on a large scale - often under incomplete information. Providers face uncertainty in many components, for example, the customer demand, the urban traffic conditions, or even the driver behavior. Mere adaptions to new information are often insufficient and anticipation of this uncertainty is key for successful operations. In research and practice, a range of anticipatory methods has been developed, usually tailored to specific practical problems. Such methods may follow intuitive rule-of-thumbs, draw on sampling procedures, or use reinforcement learning techniques. While the methods may perform well for individual problems, there is still a very limited understanding of the general dependencies of a method’s performance and a problem’s characteristics. This research project will provide this conceptual understanding.To this end, the project will systematically develop and compare different methodology for a set of problems from three different application areas, one combining urban mobility and transport as a service, one using a network of parcel stations for urban transportation, and one performing pickup and delivery with a gig economy workforce. The three problems differ in several dimensions, especially in their sources of uncertainty. To classify the problems, measures will be developed, for example, with respect to the scale of the problem or structure and degree of uncertainty. For each problem, a set of different methods will be developed. The methods will improve decision support for the specific problems while simultaneously allowing a systematic analysis of dependencies between problem and methodology performance. To this end, additional measures will be developed to classify method performance, for example, decision speed, or the interpretability of a method. Based on the problem and method measures and the extensive experiments and analyses, a framework will be developed to guide future method design for this emerging research field.This project will span six years and will be hosted at the TU München (TUM). During the project, the PI will supervise three PhD-students, each student working four years in one application area. The PI and the students will collaborate with researchers from TUM and the Georgia Institute of Technology.

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